Fundamentals of Deep Learning, 2nd Edition eBook

Fundamentals of Deep Learning, 2nd Edition e-Book:

Fundamentals of Deep Learning, 2nd Edition ebook download free book in pdf published by Oreilly Media, Author by Nithin Buduma, Nikhil Buduma, released in February 2022 (Early Access).

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception that has powered our push toward self-driving vehicles, the ability to defeat human experts at a variety of difficult games including Go and Starcraft, and even generate essays with shockingly coherent prose. But deciphering these breakthroughs often takes a Ph.D. education in machine learning and mathematics.

This updated second edition describes the intuition behind these innovations without the jargon and complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best in the field.

New chapters cover recent advancements in the fields of generative modeling and interpretability. Code examples throughout the book are updated to TensorFlow 2 and PyTorch 1.4.

In this ebook, there will cover important prerequisite knowledge that will motivate our discussion of deep learning techniques in the main text and optional material at the ends of selected chapters. Deep learning has recently experienced a renaissance, both in academic research and in the industry. It has pushed the limits of machine learning by leaps and bounds, revolutionizing fields such as computer vision and natural language processing. However, it is important to remember that deep learning is, at its core, a culmination of achievements in fields such as calculus, linear algebra, and probability. Although there are deeper connections to other fields of mathematics, we focus on the three listed here to help us broaden our perspective before diving into deep learning. These fields are key to unlocking both the big picture of deep learning and the intricate subtleties that make it as exciting as it is.